Employees

Prof. Dr. Ing. Willem Verwey

Education

Biopsychology

Traffic Psychology

Research methods

Supervising Bachelor thesis HFE

Supervising Master thesis HFE

Research

My research interest concerns the development and neurophysiological foundation of perceptual-motor skills. Why it is that we can develop such skills? And – a related interest – how can these insights be used to improve future robots and improve human-machine interfaces and training simulators?

Foundations of motor skill learning

The basic question I address concerns why repeated execution of perceptual-motor tasks, like car driving and playing the piano, automates behavior and causes a decreasing need for attention. This ability to automate behavior is essential for human behavior. How would we otherwise be able to show intelligent behavior if we would continuously need to think about each individual movement we perform? Cognitive theories (based on behavioral research) assert that repeated execution of a task induces the development of task-specific representations in human memory called motor chunks. These representations link our perceptions and our actions, and involve primarily in spatial and motor codes. These representation are used by a cognitive and a motor processor (Verwey, 2001). Together with colleagues from the A&M University in Texas , I proposed a theory how the information processing system develops and uses such representations (Verwey, Shea, & Wright, 2015). These representations allow skilled musicians to play the piano while they are having a conversation with someone. Or, to prepare forthcoming movements while earlier ones are being executed.

My experiments usually involve participants practicing movement sequences, like keying sequences in the DSP task (Figure 1), and series of aimed movements in the Flexion-Extension (FE) task. We then look how measures of behavior and brain activity change in the course of practice. For our research, we typically use the Discrete Sequence Production (DSP) task that I developed some time ago (click here to download the EPrime script I used in Verwey, 2010).

Figure 1 One way to study sequential motor learning involves the discrete sequence production (DSP) task. In this task participants initially react to the presentation of two fixed series of 6 key-specific stimuli. With practice this yields the skill to press two 6-key sequences in an almost automatic way. This task is well suited also for fMRI studies (e.g., Jouen, Verwey, et al., 2013).

Given the contemporary techniques to look into the working brain the logical next question is how these processes are based in the various structures of the brain (notably, prefrontal cortex, basal ganglia, cerebellum, supplementary motor area, and motor cortex). In other words, how are the cognitive processes, postulated on basis of behavioral research, based in the massively parallel system of the brain? Therefore, at our department we also carry out brain research using EEG, and in cooperation with external colleagues, brain scanning methods (fMRI), and stimulation of the brain using magnetic fields (TMS).

Application

Together with my colleague, prof. Frank van der Velde, I am now looking into the possibility to model our cognitive model in a neural network architecture that allows our iCub robot to learn movement patterns.

Application of the knowledge we develop with our basic research obviously is of great importance for Cognitive Ergonomics, too. To that end, I also apply the developed scientific knowledge (a) in the design of systems to make them more user-friendly, and (b) in the design of training programs and training simulators of perceptual-motor tasks like car driving, and performing surgical procedures. In practice, training simulators often appear less functional than expected, and their users do not always understand why these simulators do not work better. Our knowledge of the underlying, basic information processes help us improve training programs, determine what we can and what we cannot practice in simulators, and decide when training should stop in the simulator, and continue in the real world.

Figure 2 The iCub robot that we are providing with capabilities to learn movement patterns.